Acceleration of Iterative Image Restoration Algorithms

it to the image. Because

it to the image. Because Poisson noise is dependent on the image intensity, two tests were performed, the rst with the mean of the image as 1,000 and the second with mean 10,000. Alower image intensity results in a lower signal{to{noise ratio. Restorations were performed for 100 di erent Poisson noise processes. The results for both experiments are summarized in Table 1, which indicates image mean, whether acceleration is applied, the iteration at which the minimum MSE is achieved, the acceleration factor, the estimated number of iterations at the minimum MSE and the gure showing resulting restoration. The results for the 1,000 mean and 10,000 mean images are shown in Figs. 7 and 9 respectively, with corresponding MSE curves in Figs. 8 and 10. The MSE graphs have a solid curve as the average MSE, and the dashed curves show the maximum and minimum MSE at each iteration. (a) (b) Figure 7: R{L restoration of image corrupted with Poisson noise (Image mean = 1,000); (a) Unaccelerated RL0(32), (b) Accelerated RL1(11). Error 0.8 0.6 0.4 0.2 Min MSE MSE Curves for Poisson Noise (Image mean=1,000) Accelerated Unaccelerated 0 0 5 10 15 20 25 Iterations 30 35 40 45 50 Figure 8: MSE curves of restoration for Fig. 7. Image mean, 1,000. Results are averaged over 100 di erent Poisson noise processes; average MSE (solid curve) with maximum and minimum MSE (dashed curves) at each iteration. Minimun unaccelerated MSE, dotted curve. Owing to the high level of noise in the rst experiment, relatively few iterations can be performed before noise ampli cation becomes signi cant. Despite this, the acceleration method was still successful in reducing the number of iterations by a factor of three. The image with mean 10,000, and therefore higher signal{to{noise ratio, allowed more iterations to be performed. The accelerated algorithm works just as well in the presence of noise as the unaccelerated algorithm because a similar minimum MSE is achieved. The normal practice of halting the restoration after a certain number of iterations is important because the acceleration can cause the restoration to quickly diverge from the desired results if too many iterations are performed. (a) (b) Figure 9: R{L restoration of image corrupted with Poisson noise (Image mean, 10,000); (a) Unaccelerated RL0(151), (b) accelerated RL1(26). Error 0.8 0.6 0.4 0.2 Min MSE MSE Curves for Poisson Noise (Image mean=10,000) Accelerated Unaccelerated 0 0 20 40 60 80 100 Iterations 120 140 160 180 200 Figure 10: MSE curves of restoration for Fig. 9. Image mean, 10,000. Results averaged over 100 di erent Poisson noise processes. Curves have same de nitions as in Fig. 8. Table 1: Results ofRestoration with Poisson Noise Image Accel. Min MSE Accel. Est. Fig. Mean at Iter. Factor Iter. 1,000 No 32 { { 7(a) 1,000 Yes 11 3 32 7(b) 10,000 No 151 9 { { 9(a) 10,000 Yes 26 1 5.8 138 9(b) 3.3 Accelerationof Maximum Entropy Algorithm The new acceleration technique was applied to an ME algorithm as described by Bonavito et al [15]. The method uses Lagrange multipliers ( k) to determine the restored image (fk) such that fk = c exp(h? k); (19) where c is a scaling factor. An example restoration in [15] is performed on a Hubble Space Telescope (HST) image that su ers from spherical abberation and detector saturation 1 . The R Aquarii image (HST dataset x0c90101t cvt.c1h) is shown in Fig. 11(a) with an overexposed central region and Reseau marks visible. Those areas of invalid data are masked, and the resulting image is shown in Fig. 11(b). The HST image was obtained before installation of the corrective optics, and the PSF used for restoration was provided (HST dataset x0cj010bt cvt.c1h). Figure 11(c) shows the result of 1,000 unaccelerated iterations of the ME method. This is the same number 1 The image of the binary stellar system R Aquarii was observed with the faint object camera and made available through the Science Assessment and Early Release Observations programme of the Space Telescope Science Institute. APPLIED OPTICS / Vol. 36, No. 8 / 10 March 1997 / pp. 1766{1775 / Page 6

as performed in [15] and a similar restoration is produced. An accelerated ME restoration was performed for 50 iterations and the result is shown in Fig. 11(d). Both restorations produce comparable results, indicating that the accelerated algorithm reduced the number of iterations by a factor of approximately 20. The large acceleration factor is con rmed experimentally when it is noted that the acceleration parameter was approximately unity throughout the restoration. It is important to note that the Lagrangian multipliers ( k) are the controlling variable in the ME algorithm and, therefore, these should be accelerated rather than the pixel values of the restored image. (a) (b) (c) Figure 11: Restorationof R Aquarii using ME. (a) Raw HST data, (b) mask applied to invalid data, (c) unaccelerated (1,000 iterations), (d) accelerated (50 iterations). 3.4 Accelerationof the Gerchberg{Saxton Algorithm The Gerchberg{Saxton (G{S) algorithm is one popular method for attempting Fourier magnitude or phase retrieval. This algorithm can be painfully slow toconverge [13] and is a good candidate for applying acceleration. To test the G{S algorithm, the original F16 image was padded with zeros to 256 256 pixels, and then either the Fourier magnitude or phase was removed. Figure 12(a) shows the result of magnitude retrieval by the generic G{S algorithm after 1,000 iterations, and Fig. 12(b) shows the result after 70 accelerated iterations. The accelerated result contains fewer artifacts than 1,000 unaccelerated iterations, indicating a speedup of over 14 times. This acceleration factor is greater than that predicted analytically, possibly because the acceleration helps avoid situations in which the G{S algorithm stagnates. Phase retrieval is more di cult to achieve, as the standard G{S algorithm often stagnates. Applying accelerated G{S phase retrieval to the test image caused the (d) algorithm to stagnate faster, achieving a lower MSE in seven iterations than the unaccelerated G{S did in 100 iterations. One method of preventing stagnation is to use the hybrid input{output algorithm. There has been some preliminary success in achieving acceleration with this modi cation of the G{S algorithm. It is important to note that acceleration was applied in the frequency domain for phase retrieval and in the spatial domain for magnitude retrieval. (a) (b) Figure 12: Magnitude retrieval using the Gerchberg{Saxton algorithm; (a) Unaccelerated (1,000 iterations), (b) accelerated (70 iterations) 3.5 Practical Considerations When Applying Acceleration When applying the acceleration techniques described in this paper, there are several important considerations that should be made to ensure the best result is achieved. The rst is whether the iterative algorithm can be accelerated. Several test restorations should be made rst to observe whether the restoration converges smoothly. The path an individual pixel (or other variable) takes should be smooth and continuous with no discontinuities or noise. The algorithm should also be insensitive to small changes in the image between each iteration. Algorithms that require several hundred or several thousand iterations should achieve greater acceleration factors than those that require only a few iterations. Special consideration should be made regarding which part of the iterative algorithm is to be accelerated. When the R{L algorithm is used, the estimated restoration after each iteration is accelerated, whereas the Lagrangian multipliers [Eq. 19] are accelerated for the ME algorithm. Acceleration is normally begun immediately if a good estimate is used as the starting point for the restoration. However, if constant or random valued data is used as the rst estimate then, to prevent excessive acceleration at the start, a few unaccelerated iterations should be performed before acceleration is applied. Stability during restoration will be maintained with technique II if k is between 0 and 1, and the image retains non{negativity. Acheck ateach iteration should be made to ensure an accelerated pixel does not become negative. 3.6 Future Research There are several areas for further investigation into the new acceleration method (technique II). Applying the acceleration method to other iterative algorithms should help to uncover limitations of the method and will show APPLIED OPTICS / Vol. 36, No. 8 / 10 March 1997 / pp. 1766{1775 / Page 7

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